import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class SCREAMING_SNAKE_CASE_		:







   def __init__(     self						:						str   ,   lowerCamelCase_						:						str = "cpu"   ,   lowerCamelCase_						:						str = "openai/clip-vit-large-patch14"				):





    """simple docstring"""


    UpperCamelCase			  =      device
    UpperCamelCase			  =      CLIPTokenizerFast.from_pretrained(lowerCamelCase_				)
    UpperCamelCase			  =      [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]
    UpperCamelCase			  =      [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]
    UpperCamelCase			  =      torchvision.transforms.Normalize(self.image_mean   ,   self.image_std				)
    UpperCamelCase			  =      torchvision.transforms.Resize(224				)
    UpperCamelCase			  =      torchvision.transforms.CenterCrop(224				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self.resize(lowerCamelCase_				)
    UpperCamelCase			  =      self.center_crop(lowerCamelCase_				)
    UpperCamelCase			  =      self.normalize(lowerCamelCase_				)
    return images







   def __call__(     self						:						List[Any]   ,   lowerCamelCase_						:						Union[str, Any]=None   ,   lowerCamelCase_						:						Any=None   ,   **lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.tokenizer(text=lowerCamelCase_   ,   **lowerCamelCase_				)
    UpperCamelCase			  =      self.preprocess_img(lowerCamelCase_				)
    UpperCamelCase			  =      {key: value.to(self.device				) for (key, value) in encoding.items()}
    return encoding





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Tuple   ,   lowerCamelCase_						:						str=10   ,   lowerCamelCase_						:						Any=0.0_1   ,   lowerCamelCase_						:						List[str]=None   ,   lowerCamelCase_						:						Optional[Any]=None   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						str=None   ,   lowerCamelCase_						:						int=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						Any=False   ,   lowerCamelCase_						:						int=True   ,   lowerCamelCase_						:						Union[str, Any]="image"   ,   lowerCamelCase_						:						Tuple=True   ,   lowerCamelCase_						:						Optional[Any]=False   ,   lowerCamelCase_						:						Optional[int]=False   ,   lowerCamelCase_						:						Any=False   ,   ):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      None
    UpperCamelCase			  =      device if device else get_device()
    if vqgan:
     UpperCamelCase			  =      vqgan
    else:
     UpperCamelCase			  =      load_vqgan(self.device   ,   conf_path=lowerCamelCase_   ,   ckpt_path=lowerCamelCase_				)
    self.vqgan.eval()
    if clip:
     UpperCamelCase			  =      clip
    else:
     UpperCamelCase			  =      CLIPModel.from_pretrained("""openai/clip-vit-base-patch32"""				)
    self.clip.to(self.device				)
    UpperCamelCase			  =      ProcessorGradientFlow(device=self.device				)

    UpperCamelCase			  =      iterations
    UpperCamelCase			  =      lr
    UpperCamelCase			  =      log
    UpperCamelCase			  =      make_grid
    UpperCamelCase			  =      return_val
    UpperCamelCase			  =      quantize
    UpperCamelCase			  =      self.vqgan.decoder.z_shape







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						List[str]=5   ,   lowerCamelCase_						:						Optional[int]=True				):





    """simple docstring"""


    UpperCamelCase			  =      []
    if output_path is None:
     UpperCamelCase			  =      """./animation.gif"""
    if input_path is None:
     UpperCamelCase			  =      self.save_path
    UpperCamelCase			  =      sorted(glob(input_path + """/*"""				)				)
    if not len(lowerCamelCase_				):
     raise ValueError(
         """No images found in save path, aborting (did you pass save_intermediate=True to the generate"""
         """ function?)"""				)
    if len(lowerCamelCase_				) == 1:
     print("""Only one image found in save path, (did you pass save_intermediate=True to the generate function?)"""				)
    UpperCamelCase			  =      total_duration / len(lowerCamelCase_				)
    UpperCamelCase			  =      [frame_duration] * len(lowerCamelCase_				)
    if extend_frames:
     UpperCamelCase			  =      1.5
     UpperCamelCase			  =      3
    for file_name in paths:
     if file_name.endswith(""".png"""				):
      images.append(imageio.imread(lowerCamelCase_				)				)
    imageio.mimsave(lowerCamelCase_   ,   lowerCamelCase_   ,   duration=lowerCamelCase_				)
    print(f"""gif saved to {output_path}"""				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[str]=None   ,   lowerCamelCase_						:						Optional[Any]=None				):





    """simple docstring"""


    if not (path or img):
     raise ValueError("""Input either path or tensor"""				)
    if img is not None:
     raise NotImplementedError
    UpperCamelCase			  =      preprocess(Image.open(lowerCamelCase_				)   ,   target_image_size=256				).to(self.device				)
    UpperCamelCase			  =      preprocess_vqgan(lowerCamelCase_				)
    UpperCamelCase  ,     *UpperCamelCase			  =      self.vqgan.encode(lowerCamelCase_				)
    return z







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      self.latent.detach().requires_grad_()
    UpperCamelCase			  =      base_latent + transform_vector
    if self.quantize:
     UpperCamelCase  ,     *UpperCamelCase			  =      self.vqgan.quantize(lowerCamelCase_				)
    else:
     UpperCamelCase			  =      trans_latent
    return self.vqgan.decode(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						Union[str, Any]=None				):





    """simple docstring"""


    UpperCamelCase			  =      self.clip_preprocessor(text=lowerCamelCase_   ,   images=lowerCamelCase_   ,   return_tensors="""pt"""   ,   padding=lowerCamelCase_				)
    UpperCamelCase			  =      self.clip(**lowerCamelCase_				)
    UpperCamelCase			  =      clip_outputs.logits_per_image
    if weights is not None:
     UpperCamelCase			  =      similarity_logits * weights
    return similarity_logits.sum()







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      self._get_clip_similarity(pos_prompts["""prompts"""]   ,   lowerCamelCase_   ,   weights=(1 / pos_prompts["""weights"""])				)
    if neg_prompts:
     UpperCamelCase			  =      self._get_clip_similarity(neg_prompts["""prompts"""]   ,   lowerCamelCase_   ,   weights=neg_prompts["""weights"""]				)
    else:
     UpperCamelCase			  =      torch.tensor([1]   ,   device=self.device				)
    UpperCamelCase			  =      -torch.log(lowerCamelCase_				) + torch.log(lowerCamelCase_				)
    return loss







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Optional[int]   ,   lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      torch.randn_like(self.latent   ,   requires_grad=lowerCamelCase_   ,   device=self.device				)
    UpperCamelCase			  =      torch.optim.Adam([vector]   ,   lr=self.lr				)

    for i in range(self.iterations				):
     optim.zero_grad()
     UpperCamelCase			  =      self._add_vector(lowerCamelCase_				)
     UpperCamelCase			  =      loop_post_process(lowerCamelCase_				)
     UpperCamelCase			  =      self._get_CLIP_loss(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)
     print("""CLIP loss"""   ,   lowerCamelCase_				)
     if self.log:
      wandb.log({"""CLIP Loss""": clip_loss}				)
     clip_loss.backward(retain_graph=lowerCamelCase_				)
     optim.step()
     if self.return_val == "image":
      yield custom_to_pil(transformed_img[0]				)
     else:
      yield vector







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    wandb.init(reinit=lowerCamelCase_   ,   project="""face-editor"""				)
    wandb.config.update({"""Positive Prompts""": positive_prompts}				)
    wandb.config.update({"""Negative Prompts""": negative_prompts}				)
    wandb.config.update({"""lr""": self.lr, """iterations""": self.iterations}				)
    if image_path:
     UpperCamelCase			  =      Image.open(lowerCamelCase_				)
     UpperCamelCase			  =      image.resize((256, 256)				)
     wandb.log("""Original Image"""   ,   wandb.Image(lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    if not prompts:
     return []
    UpperCamelCase			  =      []
    UpperCamelCase			  =      []
    if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [prompt.strip() for prompt in prompts.split("""|"""				)]
    for prompt in prompts:
     if isinstance(lowerCamelCase_   ,   (tuple, list)				):
      UpperCamelCase			  =      prompt[0]
      UpperCamelCase			  =      float(prompt[1]				)
     elif ":" in prompt:
      UpperCamelCase  ,     UpperCamelCase			  =      prompt.split(""":"""				)
      UpperCamelCase			  =      float(lowerCamelCase_				)
     else:
      UpperCamelCase			  =      prompt
      UpperCamelCase			  =      1.0
     processed_prompts.append(lowerCamelCase_				)
     weights.append(lowerCamelCase_				)
    return {
        "prompts": processed_prompts,
        "weights": torch.tensor(lowerCamelCase_   ,   device=self.device				),
    }







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						List[str]=None   ,   lowerCamelCase_						:						str=True   ,   lowerCamelCase_						:						List[Any]=False   ,   lowerCamelCase_						:						int=True   ,   lowerCamelCase_						:						List[str]=True   ,   lowerCamelCase_						:						List[str]=None   ,   ):





    """simple docstring"""


    if image_path:
     UpperCamelCase			  =      self._get_latent(lowerCamelCase_				)
    else:
     UpperCamelCase			  =      torch.randn(self.latent_dim   ,   device=self.device				)
    if self.log:
     self._init_logging(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)

    assert pos_prompts, "You must provide at least one positive prompt."
    UpperCamelCase			  =      self.process_prompts(lowerCamelCase_				)
    UpperCamelCase			  =      self.process_prompts(lowerCamelCase_				)
    if save_final and save_path is None:
     UpperCamelCase			  =      os.path.join("""./outputs/"""   ,   """_""".join(pos_prompts["""prompts"""]				)				)
    if not os.path.exists(lowerCamelCase_				):
     os.makedirs(lowerCamelCase_				)
    else:
     UpperCamelCase			  =      save_path + """_""" + get_timestamp()
     os.makedirs(lowerCamelCase_				)
    UpperCamelCase			  =      save_path

    UpperCamelCase			  =      self.vqgan.decode(self.latent				)[0]
    if show_intermediate:
     print("""Original Image"""				)
     show_pil(custom_to_pil(lowerCamelCase_				)				)

    UpperCamelCase			  =      loop_post_process(lowerCamelCase_				)
    for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)				):
     if show_intermediate:
      show_pil(lowerCamelCase_				)
     if save_intermediate:
      transformed_img.save(os.path.join(self.save_path   ,   f"""iter_{iter:03d}.png"""				)				)
     if self.log:
      wandb.log({"""Image""": wandb.Image(lowerCamelCase_				)}				)
    if show_final:
     show_pil(lowerCamelCase_				)
    if save_final:
     transformed_img.save(os.path.join(self.save_path   ,   f"""iter_{iter:03d}_final.png"""				)				)

def 			lowercase(	UpperCamelCase_ = 100       )		->	int:







 '''simple docstring'''
 UpperCamelCase			  =      n * (n + 1) * (2 * n + 1) / 6
 UpperCamelCase			  =      (n * (n + 1) / 2) ** 2
 return int(square_of_sum - sum_of_squares       )


if __name__ == "__main__":
     print(F'''{solution() = }''')
